When people think of data science, they imagine men in suits crunching numbers at a computer. But the science of information has gone well beyond this in recent years. Machines themselves are coming to life, all thanks to the usefulness and application of data. Data is the lifeblood of digital intelligence.
In the past, companies that wanted smart systems had to craft them from scratch. Computers could only do what the programmer told them to do. If they came across a novel situation, they had no way to interpret and process it anew, or adapt.
But with new data-based techniques, like machine learning, deep learning, and the application of neural networks, machines are starting to think a little bit more like us. No, they’re not there yet, but we don’t need them to be 100 percent human to be valuable. There are already a host of economically-significant applications of data and AI that are making us all richer.
Data Science Is Improving Crop Yields
Farming probably isn’t the first place you expect data science to be having an economically significant effect. But if you look at the data, that’s precisely what you find. New, sophisticated techniques you can learn about if you click here are pushing up productivity and increasing the quantity of food farmers can get from an acre of land.
The new data revolution in farming has come about because of the integration of sensors with smart software. Sensors placed strategically in fields feedback to a central computer about the state of the soil, the temperature, and a bunch of other variables, providing farmers with real-time information on the needs of their crops. Farmers then receive this information through software which tells them what to do next, taking the guesswork out of the process. In the future, you can imagine the farmer being omitted entirely and drones or automated tractors carrying out the work on his or her behalf.
Data Science Is Automating Finance
Fraud investigation is a labor-intensive activity. It takes a long time to trace transactions, find patterns and identify culprits. It’s a bit like finding a needle in a haystack.
But dealing with fraud is an important task. It’s estimated that around 25 percent of all transactions that go through financial hubs like London are related to fraud in some way. Detecting fraud is something that machines find a lot easier than people because of their ability to trawl vast troves of data for patterns. Machine learning algorithms can learn what fraud looks like on the level of the data and then send alerts to human operators who can then follow up leads. Machines still can’t confirm fraud has taken place, but they can flag suspicious activity.
Data Science Is Making Legal Advice Cheaper
Lawyers spend a lot of time looking through case law and reports for precedents. It’s a time-consuming process which pushes up legal bills. But it’s also a necessary one. Now, though, machine learning can search through thousands of documents in seconds and bring up relevant information for consultation. In short, that means more people can afford to use legal services, which could have significant economic effects.
